> An important paradigm of natural language processing consists of large-scale pre-training on general domain data and adaptation to particular tasks or domains. As we pre-train larger models, full fine-tuning, which retrains all model parameters, becomes less feasible. Using GPT-3 175B as an example -- deploying independent instances of fine-tuned models, each with 175B parameters, is prohibitively expensive. We propose Low-Rank Adaptation, or LoRA, which freezes the pre-trained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared to GPT-3 175B fine-tuned with Adam, LoRA can reduce the number of trainable parameters by 10,000 times and the GPU memory requirement by 3 times. LoRA performs on-par or better than fine-tuning in model quality on RoBERTa, DeBERTa, GPT-2, and GPT-3, despite having fewer trainable parameters, a higher training throughput, and, unlike adapters, no additional inference latency. We also provide an empirical investigation into rank-deficiency in language model adaptation, which sheds light on the efficacy of LoRA. We release a package that facilitates the integration of LoRA with PyTorch models and provide our implementations and model checkpoints for RoBERTa, DeBERTa, and GPT-2 at this https URL.

>Fine-tune Stable diffusion models twice as fast than dreambooth method, by Low-rank Adaptation

> U-Net の一部のみ fine tune する

>出来上がるもの単体では使用することができず、既存のモデルとマージして使う。

>@kohya_tech: WebUI用のLoRA extensionできた(･∀･) リポジトリを作ってドキュメントを書いたら明日公開します。

>あまり努力したくないのでモデルファイル指定欄は単なるテキストボックスだし、数も五個で固定だけど、いやーやればできるもんだな……。

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